Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Election/Restrictions
Applicant’s election of group I, reading on claims 1-9, and species A1 in the reply filed on 3/13/2026 is acknowledged. Because applicant did not distinctly and specifically point out the supposed errors in the restriction requirement, the election has been treated as an election without traverse (MPEP § 818.01(a)).
Claims 10-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected groups II and III, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 3/13/2026.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-9 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by English translation of JP20000300968 by Yamadera et al. (Yamadera).
Regarding limitations recited in the claims which are directed to a manner of operating disclosed learning model generation device, it is noted that neither the manner of operating a disclosed device nor material or article worked upon further limit an apparatus claim. Said limitations do not differentiate apparatus claims from prior art. See MPEP § 2114 and 2115. "[A]pparatus claims cover what a device is, not what a device does." Hewlett-Packard Co. v. Bausch & Lomb Inc., 909 F.2d 1464, 1469, 15 USPQ2d 1525, 1528 (Fed. Cir. 1990) (emphasis in original). A claim containing a "recitation with respect to the manner in which a claimed apparatus is intended to be employed does not differentiate the claimed apparatus from a prior art apparatus" if the prior art apparatus teaches all the structural limitations of the claim. Ex parte Masham, 2 USPQ2d 1647 (Bd. Pat. App. & Inter. 1987). Claim analysis is highly fact-dependent. A claim is only limited by positively recited elements. Thus, "[i]nclusion of the material or article worked upon by a structure being claimed does not impart patentability to the claims." In re Otto, 312 F.2d 937, 136 USPQ 458, 459 (CCPA 1963); see also In re Young, 75 F.2d 996, 25 USPQ 69 (CCPA 1935). This applies to the following limitations: “configured to acquire input data derived from operation data that is measured during a membrane filtration operation” (claim 1); “operation data including a membrane filtration pressure and a diffused air volume” (claim 1); “disposed so as to be immersed in a water to be treated” (claim 1); “configured to perform air diffusion through a membrane” (claim 1); “the membrane separation device being configured to obtain a treated water that has passed through the separation membrane while causing the air diffusion device to perform the air diffusion” (claim 1); “configured to generate a learning model for inferring a state of the separation membrane by means of machine learning using the acquired input data as an input” (claim 1); “the membrane filtration operation is an intermitted operation” (claim 2); “ the input data derived from the membrane filtration pressure includes at least one selected from the group consisting of a maximum value of the membrane filtration pressure, a minimum value of the membrane filtration pressure, a standard deviation value of the membrane filtration pressure, an average value of the membrane filtration pressure, and a transmembrane pressure” (claim 2); “unit period consisting of an operation period and a pause period that follows the operation period” (claim 2); “a fluctuation rate of the transmembrane pressure which area in a predetermined period before the unit period” (claim 2); “the input data derived from the diffused air volume includes at least one selected from the group consisting of an average value of the diffused air volume in the unit period and an integrated value of the diffused air volume in the predetermined period” (claim 2); “the operation data further includes a membrane filtration flow rate that is measured during the membrane filtration operation” (claim 3); “the membrane filtration operation is an intermittent operation” (claim 4); “the input data derived from the membrane filtration flow rate includes at least one selected from the group consisting of an average value of the membrane filtration flow rate in a unit period consisting of an operation period and a pause period that follows the operation period and an integrated value of the membrane filtration flow rate in a predetermined period before the unit period” (claim 4); “configured to generate training data in which the input data and a label indicating the state of the separation membrane with respect to the input data are associated with each other” (claim 5); “the learning section generates the learning model by means of supervised learning using the generated training data” (claim 5); “the label includes a normality label associated with the input data in a case where the input data indicates that the state of the separation membrane will become normal, and an anomaly label which is associated with the input data in a case where the input data indicates that the state of the separation membrane will become anomalous” (claim 6); “the label further includes an intermediate label which is associated with the input data in a case where the input data indicates that the state of the separation membrane will become an intermedia state between normality and anomaly” (claim 7); “the learning section generates, by means of unsupervised learning, the learning model which includes clusters as a learning result” (claim 8); “the learning section acquires data inputted to the learning model when the state of the separation membrane is inferred with use of the learning model” (claim 9); “the learning model by means of machine learning using the acquired data as an input” (claim 9).
In regard to claim 1, Yamadera teaches a learning model generation device ([0013]-[0034], especially [0031]). Yamadera teaches an input data acquisition section configured to acquire input data derived from operation data that is measured during a membrane filtration operation which is carried out by a membrane separation device ([0013]-[0034]). Yamadera teaches the operation data including a membrane filtration pressure and a diffused air volume ([0013]-[0034]). Yamadera teaches the membrane separation device comprising a separation membrane disposed so as to be immersed in a water to be treated ([0013]-[0034]). Yamadera teaches an air diffusion device configured to perform air diffusion through a membrane surface of the separation membrane ([0013]-[0034]). Yamadera teaches the membrane separation device being configured to obtain a treated water that has passed through the separation membrane while causing the air diffusion device to perform the air diffusion ([0013]-[0034]). Yamadera teaches a learning section configured to generate a learning model for inferring a state of the separation membrane by means of machine learning using the acquired input data as an input ([0013]-[0034], especially [0031]).
In regard to claim 2, Yamadera teaches the membrane filtration operation is an intermitted operation ([0020]). Yamadera teaches the input data derived from the membrane filtration pressure includes at least one selected from the group consisting of a maximum value of the membrane filtration pressure, a minimum value of the membrane filtration pressure, a standard deviation value of the membrane filtration pressure, an average value of the membrane filtration pressure, and a transmembrane pressure ([0013]-[0034], [0016], filtration pressure, pressure difference between the outside and inside of the membrane). Yamadera teaches a unit period consisting of an operation period and a pause period that follows the operation period ([0020]). Yamadera teaches a fluctuation rate of the transmembrane pressure which area in a predetermined period before the unit period ([0008]; [0013]-[0034]). Yamadera teaches the input data derived from the diffused air volume includes at least one selected from the group consisting of an average value of the diffused air volume in the unit period and an integrated value of the diffused air volume in the predetermined period ([0013]-[0034]).
In regard to claim 3, Yamadera teaches the operation data further includes a membrane filtration flow rate that is measured during the membrane filtration operation ([0005]-[0006]; [0009]-[0011]; [0013]-[0034]).
In regard to claim 4, Yamadera teaches the membrane filtration operation is an intermittent operation ([0013]-[0034]). Yamadera teaches the input data derived from the membrane filtration flow rate includes at least one selected from the group consisting of an average value of the membrane filtration flow rate in a unit period consisting of an operation period and a pause period that follows the operation period and an integrated value of the membrane filtration flow rate in a predetermined period before the unit period ([0013]-[0034]).
In regard to claim 5, Yamadera teaches a training data generation section configured to generate training data in which the input data and a label indicating the state of the separation membrane with respect to the input data are associated with each other ([0013]-[0034]; [0048]). Yamadera teaches the learning section generates the learning model by means of supervised learning using the generated training data ([0013]-[0034]; [0048]).
In regard to claim 6, Yamadera teaches the label includes a normality label, associated with the input data in a case where the input data indicates that the state of the separation membrane will become normal, and an anomaly label which is associated with the input data in a case where the input data indicates that the state of the separation membrane will become anomalous (abstract; [0008]-[0034]).
In regard to claim 7, Yamadera teaches the label further includes an intermediate label which is associated with the input data in a case where the input data indicates that the state of the separation membrane will become an intermedia state between normality and anomaly (abstract; [0008]-[0034]).
In regard to claim 8, Yamadera teaches the learning section generates, by means of unsupervised learning, the learning model which includes clusters as a learning result (abstract; [0013]-[0034]).
In regard to claim 9, Yamadera teaches the learning section acquires data inputted to the learning model when the state of the separation membrane is inferred with use of the learning model ([0013]-[0034]; [0048]). Yamadera teaches updates the learning model by means of machine learning using the acquired data as an input ([0013]-[0034]; ]0048]).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite the limitations “acquire input data”, “operation data”, “generate a learning model for inferring a state of the separation membrane by means of machine learning using the acquired input data as an input” which are mathematical concept or metal process. Claim 1 recites acquiring input data derived from operation data. Although acquiring data could be considered abstract in the form of a mental process, the fact that the acquired data is derived from the operation data also implies that some type of mental process and/or math is changing the operation data into the derived acquired data. If the input data acquisition section is interpreted as a controller or computer, then the abstract ideas are performed by a computer. MPEP 2106.04(a)(2)III is clear that using a computer/controller to perform the abstract idea does not preclude the steps from being considered an abstract idea. The claim also recites generating a learning model by machine learning using the acquired data, which is an abstract idea. Machine learning is an abstract idea and is doing what the brain can do but faster.
This judicial exception is not integrated into a practical application because the additional elements individually and in combination do not integrate the judicial exception into a practical application. Once the acquired input data is derived then no action is taken. Further, once the machine learning is performed, then no further action is taken. Therefore, there is no particular practical application. Further, the generating the learning model using the acquired data just seems like generally “applying” the abstract idea per MPEP 2106.05(f).
The claims recite acquiring operation data, but that is just data gathering which is insignificant extra solution activity and not a particular practical application. See MPEP 2106.05(g).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the input data acquisition section and learning section appear to just be computers and are considered well-understood, routine, and conventional. The separation membrane and air diffusion device are additionally well-understood, routine, and conventional.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KARA M PEO whose telephone number is (571)272-9958. The examiner can normally be reached 9 to 5:30.
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/KARA M PEO/Primary Examiner, Art Unit 1777
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